Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. Bloom Richardson grading is the most commonly used grading system for histopathologic diagnosis of invasive BCa. A key component of Bloom Richardson grading is mitotic count. Mitotic count, which refers to the number of dividing cells (e.g., mitoses) visible in a given area of hematoxylin and eosin (H&E) stained images, is an effective predictor of disease aggressiveness. Clinically, mitotic count is the number of mitotic nuclei identified visually in a fixed number of high power fields (HPF). Conventionally, mitotic nuclei are identified manually by a pathologist. Manual identification of mitotic nuclei suffers from poor inter-interpreter agreement due to the variable texture and morphology between mitoses. Manual identification is also a resource intensive and time consuming process that involves a trained pathologist manually inspecting and counting cells viewed in an HPF under a microscope. Manual identification is not optimal when trying to bring treatments to bear on a patient as quickly as possible in a clinically relevant timeframe.
Computerized detection of mitotic nuclei attempts to increase the speed, accuracy, and consistency of mitotic identification. However, the detection of mitotic nuclei in an H&E stained slide is a challenging task for an automated system. During mitosis, the cell nucleus undergoes various morphological transformations that lead to highly variable sizes and shapes across mitotic nuclei within the same image. Automated detection of mitotic nuclei is further complicated by rare event detection. Rare event detection complicates classification when one class (e.g., mitotic nuclei) is substantially less prevalent than the other class (e.g., non-mitotic nuclei).
Conventional approaches to computerized mitotic detection that employ manual annotation of candidate regions by an expert pathologist offer only limited improvements over manual detection, and still suffer from the problem of inter-interpreter disagreement. Some conventional approaches to computerized mitotic detection that try to minimize reliance on a human pathologist may employ machine learning techniques. For example, some conventional approaches to computerized mitotic detection feature machine learning systems and methods. These systems and methods employ convolutional neural networks (CNN) to identify features and assist in mitotic detection. However, conventional CNN methods are computationally demanding. For example, one conventional method for mitotic detection employs an eleven-layered CNN. Since each layer is comprised of hundreds of units, this conventional method takes several days to analyze an image. Other conventional methods that employ CNN may take several weeks to train and test a classifier. Several weeks may be a sub-optimal time frame when administering timely treatment to a patient suffering from an aggressive form of cancer.
Conventional methods of automatic mitotic detection may employ handcrafted (HC) features. HC features identified by conventional techniques include various morphological, statistical, and textural features that attempt to model the appearance of mitosis in digitized images. However, while HC-feature-based classifiers may be faster than CNN-based classifiers, conventional HC feature-based classifiers are not as accurate as CNN-based classifiers, and fail to identify some features that CNN-based classifiers may detect. Conventional HC-based classifiers are highly dependent on the evaluation dataset used to train the HC-based classifier. Furthermore, HC-based classifiers lack a principled approach for combining disparate features. Thus, conventional CNN-based classification systems and methods of automatic mitotic detection are computationally intensive and may operate in time frames that are not optimal for clinical relevance when diagnosing patients (e.g., days or weeks instead of hours or minutes). Conventional HC-based classifiers, while faster than CNN-based classifiers, are not as accurate as CNN-based classifiers, suffer from a strong dependence on the training dataset, and are not optimally suited for combining disparate features.